Trevor Campbell
31 papers · 2013–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (6) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (13) π§ Keyword Pioneer π Academic Marathon (12)
π
Interdisciplinary Bridge
π
Conference Polyglot
(6)
π
Academic Marathon
(12)
π¬
Deep Specialist
(18)
π
Keyword Champion
(5)
ποΈ
Keyword Collector
(106)
β‘
Prolific Year
(5)
π
Century Club
(31)
π₯
Unstoppable
(11)
π
Trend Setter
β
The Questioner
Conferences
NIPS (12)
AISTATS (7)
ICML (6)
UAI (3)
CVPR (2)
JMLR (1)
Top co-authors
Research topics
Keywords
bayesian inference
(11)
variational inference
(9)
markov chain monte carlo
(8)
posterior approximation
(8)
bayesian nonparametrics
(5)
posterior inference
(5)
bayesian coreset
(4)
uncertainty quantification
(3)
kl divergence
(2)
parallel tempering
(2)
variational flow
(2)
normalizing flow
(2)
measure-preserving map
(2)
importance sampling
(2)
stochastic process
(2)
coreset construction
(2)
density evaluation
(2)
nonparametric bayesian
(1)
graph theory
(1)
model misspecification
(1)
Papers
Is Gibbs sampling faster than Hamiltonian Monte Carlo on GLMs?
AISTATS 2025
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
UAI 2025
AutoStep: Locally adaptive involutive MCMC
ICML 2025
Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization
ICML 2025
Mixed variational flows for discrete variables
AISTATS 2024
General bounds on the quality of Bayesian coresets
NIPS 2024
Coreset Markov chain Monte Carlo
AISTATS 2024
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
AISTATS 2024
Embracing the chaos: analysis and diagnosis of numerical instability in variational flows
NIPS 2023
MixFlows: principled variational inference via mixed flows
ICML 2023
Parallel Tempering With a Variational Reference
NIPS 2022
Bayesian inference via sparse Hamiltonian flows
NIPS 2022
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement
NIPS 2022
Parallel tempering on optimized paths
ICML 2021
Finite mixture models do not reliably learn the number of components
ICML 2021
Sequential core-set Monte Carlo
UAI 2021
Bayesian Pseudocoresets
NIPS 2020
Slice Sampling for General Completely Random Measures
UAI 2020
Validated Variational Inference via Practical Posterior Error Bounds
AISTATS 2020
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees
AISTATS 2019
Universal Boosting Variational Inference
NIPS 2019
Automated Scalable Bayesian Inference via Hilbert Coresets
JMLR 2019
Sparse Variational Inference: Bayesian Coresets from Scratch
NIPS 2019
Data-dependent compression of random features for large-scale kernel approximation
AISTATS 2019
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
ICML 2018
Efficient Global Point Cloud Alignment Using Bayesian Nonparametric Mixtures
CVPR 2017
Coresets for Scalable Bayesian Logistic Regression
NIPS 2016
Edge-exchangeable graphs and sparsity
NIPS 2016
Small-Variance Nonparametric Clustering on the Hypersphere
CVPR 2015
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
NIPS 2015
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
NIPS 2013